Itertools.groupby() Not Grouping Correctly

I have this data: self.data = [(1, 1, 5.0), (1, 2, 3.0), (1, 3, 4.0), (2, 1, 4.0), (2, 2, 2.0)] When I run this code: for mid,

Solution 1:

itertools.groupby collects together contiguous items with the same key. If you want all items with the same key, you have to sort self.data first.

for mid, group in itertools.groupby(
    sorted(self.data,key=operator.itemgetter(1)), key=operator.itemgetter(1)):

Solution 2:

Variant without sorting (via dictionary). Should be better performance-wise.

def full_group_by(l, key=lambda x: x):
    d = defaultdict(list)
    for item in l:
        d[key(item)].append(item)
    return d.items()

Solution 3:

Below "fixes" several annoyances with Python's itertools.groupby.

def groupby2(l, key=lambda x:x, val=lambda x:x, agg=lambda x:x, sort=True):
    if sort:
        l = sorted(l, key=key)
    return ((k, agg((val(x) for x in v))) \
        for k,v in itertools.groupby(l, key=key))

Specifically,

1. It doesn't require that you sort your data.
2. It doesn't require that you must use key as named parameter only.
3. The output is clean generator of tuple(key, grouped_values) where values are specified by 3rd parameter.
4. Ability to apply aggregation functions like sum or avg easily.

Example Usage

import itertools
from operator import itemgetter
from statistics import *

t = [('a',1), ('b',2), ('a',3)]
for k,v in groupby2(t, itemgetter(0), itemgetter(1), sum):
  print(k, v)

This prints,

a 4
b 2

Play with this code